Manager, Machine Learning Engineering & Data Science, Hyderabad

Warner Bros Discovery Warner Bros Discovery · Media · Hyderabad, Telangāna, India · Technology

Manager for a team of Machine Learning Engineers and Data Scientists responsible for building, deploying, and operating large-scale ML systems for consumer platforms, including user behavioral modeling, fraud detection, and transaction intelligence. The role involves leading the development of ML platform capabilities across the full lifecycle (pipeline orchestration, experimentation, deployment, observability) and requires hands-on technical depth in designing experiments, architecting scalable ML solutions, and building production-grade systems.

What you'd actually do

  1. Own end-to-end delivery: Accountable for delivering ML/DS initiatives from problem definition through production, ensuring timelines, quality, and business impact are consistently met.
  2. Own ML platform and lifecycle capabilities: Build and evolve core systems for ML pipelines, experimentation, model versioning/promotion, deployment, and observability, ensuring reproducibility, governance, and production reliability.
  3. Be a hands-on technical leader: Stay close to the work—review designs, guide modeling approaches, participate in key technical decisions, and help unblock complex ML and data challenges.
  4. Partner deeply with product and stakeholders: Work closely with Product, Engineering, and business teams to shape problem statements, prioritize work, and ensure ML solutions align to clear outcomes and KPIs.
  5. Drive execution with operational excellence: Establish strong planning, tracking, and risk management practices; ensure ML systems are production-ready with robust pipelines, automated deployment workflows, and end-to-end monitoring/observability.

Skills

Required

  • 12–15 years of total experience
  • 3+ years of leading and managing ML, Data Science teams
  • Proven experience designing and delivering machine learning-driven, large-scale distributed systems in production
  • Experience building or operating end-to-end ML platforms, including pipeline orchestration, experimentation systems, model registry/promotion workflows, and production observability
  • Experience translating business problems into scalable ML/AI solutions with measurable impact
  • Hands-on experience with cloud platforms (AWS, GCP, or Azure) and modern ML/data ecosystems
  • Strong understanding of ML platforms, data pipelines, distributed systems, and production architecture patterns
  • Ability to collaborate effectively across Product, Engineering, Data, and Business teams
  • Excellent written and verbal communication skills with ability to contribute to technical design discussions and documentation

Nice to have

  • Experience working with global, cross-functional teams
  • Exposure to MLOps / LLMOps frameworks, including experiment tracking, model governance, promotion strategies, deployment pipelines, and observability tooling
  • Familiarity with modern AI patterns (e.g., GenAI, LLM-based applications)

What the JD emphasized

  • large-scale ML systems
  • ML platform and lifecycle capabilities
  • production ML systems
  • scalable ML solutions
  • production-grade systems
  • production observability

Other signals

  • leading a team of ML engineers and data scientists
  • building and deploying large-scale ML systems
  • ML platform capabilities across the full lifecycle
  • production ML systems